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      "source": [
        "---\n",
        "sidebar_label: DeepSeek\n",
        "---"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "e49f1e0d",
      "metadata": {},
      "source": [
        "# ChatDeepSeek\n",
        "\n",
        "This will help you getting started with DeepSeek [chat models](/docs/concepts/#chat-models). For detailed documentation of all `ChatDeepSeek` features and configurations head to the [API reference](https://api.js.langchain.com/classes/_langchain_deepseek.ChatDeepSeek.html).\n",
        "\n",
        "## Overview\n",
        "### Integration details\n",
        "\n",
        "| Class | Package | Local | Serializable | [PY support](https://python.langchain.com/docs/integrations/chat/deepseek) | Package downloads | Package latest |\n",
        "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
        "| [`ChatDeepSeek`](https://api.js.langchain.com/classes/_langchain_deepseek.ChatDeepSeek.html) | [`@langchain/deepseek`](https://npmjs.com/@langchain/deepseek) | ❌ (see [Ollama](/docs/integrations/chat/ollama)) | beta | ✅ | ![NPM - Downloads](https://img.shields.io/npm/dm/@langchain/deepseek?style=flat-square&label=%20&) | ![NPM - Version](https://img.shields.io/npm/v/@langchain/deepseek?style=flat-square&label=%20&) |\n",
        "\n",
        "### Model features\n",
        "\n",
        "See the links in the table headers below for guides on how to use specific features.\n",
        "\n",
        "| [Tool calling](/docs/how_to/tool_calling) | [Structured output](/docs/how_to/structured_output/) | JSON mode | [Image input](/docs/how_to/multimodal_inputs/) | Audio input | Video input | [Token-level streaming](/docs/how_to/chat_streaming/) | [Token usage](/docs/how_to/chat_token_usage_tracking/) | [Logprobs](/docs/how_to/logprobs/) |\n",
        "| :---: | :---: | :---: | :---: |  :---: | :---: | :---: | :---: | :---: |\n",
        "| ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ✅ | ✅ | \n",
        "\n",
        "Note that as of 1/27/25, tool calling and structured output are not currently supported for `deepseek-reasoner`.\n",
        "\n",
        "## Setup\n",
        "\n",
        "To access DeepSeek models you'll need to create a DeepSeek account, get an API key, and install the `@langchain/deepseek` integration package.\n",
        "\n",
        "You can also access the DeepSeek API through providers like [Together AI](/docs/integrations/chat/togetherai) or [Ollama](/docs/integrations/chat/ollama).\n",
        "\n",
        "### Credentials\n",
        "\n",
        "Head to https://deepseek.com/ to sign up to DeepSeek and generate an API key. Once you've done this set the `DEEPSEEK_API_KEY` environment variable:\n",
        "\n",
        "```bash\n",
        "export DEEPSEEK_API_KEY=\"your-api-key\"\n",
        "```\n",
        "\n",
        "If you want to get automated tracing of your model calls you can also set your [LangSmith](https://docs.smith.langchain.com/) API key by uncommenting below:\n",
        "\n",
        "```bash\n",
        "# export LANGSMITH_TRACING=\"true\"\n",
        "# export LANGSMITH_API_KEY=\"your-api-key\"\n",
        "```\n",
        "\n",
        "### Installation\n",
        "\n",
        "The LangChain ChatDeepSeek integration lives in the `@langchain/deepseek` package:\n",
        "\n",
        "```{=mdx}\n",
        "import IntegrationInstallTooltip from \"@mdx_components/integration_install_tooltip.mdx\";\n",
        "import Npm2Yarn from \"@theme/Npm2Yarn\";\n",
        "\n",
        "<IntegrationInstallTooltip></IntegrationInstallTooltip>\n",
        "\n",
        "<Npm2Yarn>\n",
        "  @langchain/deepseek @langchain/core\n",
        "</Npm2Yarn>\n",
        "\n",
        "```"
      ]
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    {
      "cell_type": "markdown",
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      "metadata": {},
      "source": [
        "## Instantiation\n",
        "\n",
        "Now we can instantiate our model object and generate chat completions:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "id": "cb09c344-1836-4e0c-acf8-11d13ac1dbae",
      "metadata": {},
      "outputs": [],
      "source": [
        "import { ChatDeepSeek } from \"@langchain/deepseek\";\n",
        "\n",
        "const llm = new ChatDeepSeek({\n",
        "  model: \"deepseek-reasoner\",\n",
        "  temperature: 0,\n",
        "  // other params...\n",
        "})"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "2b4f3e15",
      "metadata": {},
      "source": [
        "<!-- ## Invocation -->"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "id": "62e0dbc3",
      "metadata": {
        "tags": []
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "AIMessage {\n",
            "  \"id\": \"e2874482-68a7-4552-8154-b6a245bab429\",\n",
            "  \"content\": \"J'adore la programmation.\",\n",
            "  \"additional_kwargs\": {,\n",
            "    \"reasoning_content\": \"...\",\n",
            "  },\n",
            "  \"response_metadata\": {\n",
            "    \"tokenUsage\": {\n",
            "      \"promptTokens\": 23,\n",
            "      \"completionTokens\": 7,\n",
            "      \"totalTokens\": 30\n",
            "    },\n",
            "    \"finish_reason\": \"stop\",\n",
            "    \"model_name\": \"deepseek-reasoner\",\n",
            "    \"usage\": {\n",
            "      \"prompt_tokens\": 23,\n",
            "      \"completion_tokens\": 7,\n",
            "      \"total_tokens\": 30,\n",
            "      \"prompt_tokens_details\": {\n",
            "        \"cached_tokens\": 0\n",
            "      },\n",
            "      \"prompt_cache_hit_tokens\": 0,\n",
            "      \"prompt_cache_miss_tokens\": 23\n",
            "    },\n",
            "    \"system_fingerprint\": \"fp_3a5770e1b4\"\n",
            "  },\n",
            "  \"tool_calls\": [],\n",
            "  \"invalid_tool_calls\": [],\n",
            "  \"usage_metadata\": {\n",
            "    \"output_tokens\": 7,\n",
            "    \"input_tokens\": 23,\n",
            "    \"total_tokens\": 30,\n",
            "    \"input_token_details\": {\n",
            "      \"cache_read\": 0\n",
            "    },\n",
            "    \"output_token_details\": {}\n",
            "  }\n",
            "}\n"
          ]
        }
      ],
      "source": [
        "const aiMsg = await llm.invoke([\n",
        "  [\n",
        "    \"system\",\n",
        "    \"You are a helpful assistant that translates English to French. Translate the user sentence.\",\n",
        "  ],\n",
        "  [\"human\", \"I love programming.\"],\n",
        "])\n",
        "aiMsg"
      ]
    },
    {
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      "execution_count": 3,
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        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "J'adore la programmation.\n"
          ]
        }
      ],
      "source": [
        "console.log(aiMsg.content)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "18e2bfc0-7e78-4528-a73f-499ac150dca8",
      "metadata": {},
      "source": [
        "## Chaining\n",
        "\n",
        "We can [chain](/docs/how_to/sequence/) our model with a prompt template like so:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "id": "e197d1d7-a070-4c96-9f8a-a0e86d046e0b",
      "metadata": {},
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        {
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          "text": [
            "AIMessage {\n",
            "  \"id\": \"6e7f6f8c-8d7a-4dad-be07-425384038fd4\",\n",
            "  \"content\": \"Ich liebe es zu programmieren.\",\n",
            "  \"additional_kwargs\": {,\n",
            "    \"reasoning_content\": \"...\",\n",
            "  },\n",
            "  \"response_metadata\": {\n",
            "    \"tokenUsage\": {\n",
            "      \"promptTokens\": 18,\n",
            "      \"completionTokens\": 9,\n",
            "      \"totalTokens\": 27\n",
            "    },\n",
            "    \"finish_reason\": \"stop\",\n",
            "    \"model_name\": \"deepseek-reasoner\",\n",
            "    \"usage\": {\n",
            "      \"prompt_tokens\": 18,\n",
            "      \"completion_tokens\": 9,\n",
            "      \"total_tokens\": 27,\n",
            "      \"prompt_tokens_details\": {\n",
            "        \"cached_tokens\": 0\n",
            "      },\n",
            "      \"prompt_cache_hit_tokens\": 0,\n",
            "      \"prompt_cache_miss_tokens\": 18\n",
            "    },\n",
            "    \"system_fingerprint\": \"fp_3a5770e1b4\"\n",
            "  },\n",
            "  \"tool_calls\": [],\n",
            "  \"invalid_tool_calls\": [],\n",
            "  \"usage_metadata\": {\n",
            "    \"output_tokens\": 9,\n",
            "    \"input_tokens\": 18,\n",
            "    \"total_tokens\": 27,\n",
            "    \"input_token_details\": {\n",
            "      \"cache_read\": 0\n",
            "    },\n",
            "    \"output_token_details\": {}\n",
            "  }\n",
            "}\n"
          ]
        }
      ],
      "source": [
        "import { ChatPromptTemplate } from \"@langchain/core/prompts\"\n",
        "\n",
        "const prompt = ChatPromptTemplate.fromMessages(\n",
        "  [\n",
        "    [\n",
        "      \"system\",\n",
        "      \"You are a helpful assistant that translates {input_language} to {output_language}.\",\n",
        "    ],\n",
        "    [\"human\", \"{input}\"],\n",
        "  ]\n",
        ")\n",
        "\n",
        "const chain = prompt.pipe(llm);\n",
        "await chain.invoke(\n",
        "  {\n",
        "    input_language: \"English\",\n",
        "    output_language: \"German\",\n",
        "    input: \"I love programming.\",\n",
        "  }\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "3a5bb5ca-c3ae-4a58-be67-2cd18574b9a3",
      "metadata": {},
      "source": [
        "## API reference\n",
        "\n",
        "For detailed documentation of all ChatDeepSeek features and configurations head to the API reference: https://api.js.langchain.com/classes/_langchain_deepseek.ChatDeepSeek.html"
      ]
    }
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